Booleans for the win!

Tijd voor een showdown! In deze oefening vergelijk je het loopen over een pandas DataFrame rechtstreeks met Boolean indexing.

Loop eerst over de rijen van het wildlife_strikes-DataFrame en print de volgende informatie over de botsing als die een Snowy egret betrof, een trekvogel uit Noord- en Zuid-Amerika:

Datum van het incident (kolom INCIDENT_DATE)
Luchthaven (kolom AIRPORT)
Type vliegtuig (kolom ATYPE)
Ernst van de schade (kolom DAMAGE)

Vergelijk deze aanpak daarna met Boolean indexing en print dezelfde informatie op het subset-DataFrame.

Deze oefening maakt deel uit van de cursus

Python voor MATLAB-gebruikers

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Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Loop through wildlife_strikes and print snowy egret data
for i, row in wildlife_strikes.____():
    if row['SPECIES']=='Snowy egret':
        print(row['INCIDENT_DATE'],
              row['AIRPORT'],
              row['ATYPE'],
              row['DAMAGE'])

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Deze oefening maakt deel uit van de cursus

Python voor MATLAB-gebruikers

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Begin de cursus gratis

This chapter gets you started moving from MATLAB to Python. You'll learn about some of the similarities and differences between MATLAB and Python, how to use methods and packages, and be introduced to the popular NumPy package.

Exercise 1: Welcome to Python!Exercise 2: Basic calculations Exercise 3: Forecasting an investment Exercise 4: Types of variables Exercise 5: Methods and packages Exercise 6: Manipulating strings with methods Exercise 7: Using Python packages Exercise 8: Arrays & plotting Exercise 9: Getting started with NumPy arrays Exercise 10: Plotting bicycle traffic Exercise 11: What predicts animal longevity?

In this chapter, you will build on your new NumPy knowledge. You will dive into NumPy arrays, the Python analog to matrices by performing mathematical operations and indexing. You will also begin to explore another important Python data structure, the list, and then round out the chapter by making customs plots of your arrays using Matplotlib.

Exercise 1: Diving into NumPy arrays Exercise 2: Forecasting over time Exercise 3: How much more do organic avocados cost?Exercise 4: Indexing NumPy arrays Exercise 5: Getting columns from NumPy arrays Exercise 6: Bike traffic throughout the week Exercise 7: Filtering arrays with Boolean indexing Exercise 8: Lists Exercise 9: List exploration Exercise 10: Making NumPy arrays from lists Exercise 11: Operating on lists and arrays Exercise 12: Customizing plots Exercise 13: Colors, linestyles, and legends Exercise 14: Encoding data in color & size Exercise 15: Determine engine types in wildlife strikes

This chapter introduces some powerful Python data structures: the dictionary and the pandas DataFrame. You will learn to create dictionaries by setting key-value pairs, and view then how to view and modify your dictionary. Then you will be introduced to one of the most important packages in the Pythonista's toolbox, pandas. Specifically, you'll focus on the pandas' structure, the DataFrame, which organizes tabular data in an easily accessible way. Lastly, you'll learn how to transform different data types into DataFrames to make your data easier to work with.

Exercise 1: Dictionaries Exercise 2: Which keys?Exercise 3: Get data out Exercise 4: Modifying dictionaries Exercise 5: Introduction to DataFrames Exercise 6: Explore a pandas DataFrame Exercise 7: Plot data from a DataFrame Exercise 8: Accessing pandas DataFrames Exercise 9: Accessing rows and columns Exercise 10: The many flavors of .iloc Exercise 11: Creating pandas DataFrames Exercise 12: From a CSV file Exercise 13: From a dictionary of lists Exercise 14: From a list of dictionaries

You'll finish the course by controlling your Python flow. You will learn how to iterate through different Python data structures using for loops. You will also learn about Python contingencies using if, else, and elif and the Python comparison operators (greater than, less than, etc.) that will decide which lines of your code will be executed. Lastly, you'll circle back to NumPy arrays by using Python comparison operators to filter your arrays.

Exercise 1: Lussen door data Exercise 2: Door lijsten itereren Exercise 3: Meerdere plots over elkaar in één figuur Exercise 4: Vergelijkingen en control flow Exercise 5: Verschillende vliegtuigtypen tellen Exercise 6: Bepaalde rijen in een DataFrame vinden Exercise 7: Data filteren Exercise 8: Booleaanse indexing voor snelle statistieken Exercise 9: Booleans for the win!

Huidige oefening

Exercise 10: Booleaanse indexering en Matplotlib-plezier Exercise 11: Goed gedaan!